{"categories":[{"label":"Utilities","url":"https://skillfed.io/packages/category/utilities/5"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/5"},{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/2"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"}],"enrichment":{"capability":"POT provides solvers for optimal transport problems, including Wasserstein distances, Gromov-Wasserstein distances, and related algorithms for signal processing, image processing, and machine learning applications.","skillfed_tags":["optimal-transport","domain-adaptation","computational-geometry"],"use_cases":["Compute Wasserstein distances and optimal transport plans between point clouds or distributions for metric learning.","Perform domain adaptation by aligning source and target distributions in machine learning pipelines.","Calculate Gromov-Wasserstein distances to compare structured data like graphs or point clouds with different geometries.","Compute Wasserstein barycenters to find representative distributions or aggregate multiple datasets.","Solve unbalanced or partial optimal transport problems when mass conservation is relaxed.","Integrate OT solvers into neural networks via differentiable layers for end-to-end learning."],"what_it_does":"POT is a Python library for solving optimal transport problems\u2014a mathematical framework for measuring and computing distances between distributions. It provides a large collection of differentiable solvers covering exact linear OT, entropic and quadratic regularized variants, Gromov-Wasserstein distances, unbalanced and partial OT, and specialized algorithms for 1D, circular, and Gaussian mixture model transport. The library also includes machine learning applications such as domain adaptation, transport mapping, and subspace learning.\n\nThe package depends on numpy and scipy for numerical computation and offers multiple backend support (PyTorch, JAX, TensorFlow, CuPy) to work with different array types. It is actively maintained, production-stable, and widely used in research and applied settings where distribution comparison, alignment, or interpolation is needed.","worth_installing":"Yes. POT is actively maintained, production-stable (Development Status 5), has no known vulnerabilities, and offers a comprehensive suite of optimal transport algorithms backed by research. The medium install friction is manageable with prebuilt wheels. Install if you need optimal transport solvers for machine learning, domain adaptation, or distribution comparison; skip if you have no use for these mathematical tools."},"id":"pot","links":{"html":"https://skillfed.io/packages/pot","md":"https://skillfed.io/packages/pot.md","pypi":"https://pypi.org/project/pot/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-29","license_spdx":null,"license_treatment":"permissive","name":"POT","python_support":"supports_current","summary":"Python Optimal Transport Library"},"popularity":{"monthly_downloads":587786,"position":5872,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.9.7.post1"}